1. Field of the Invention
The present invention relates generally to storage of digital data, and more particularly to compressing data generated over a network.
2. Description of the Related Art
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present invention, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Compression of digital data has become an indispensable tool in almost every computing platform adapted to store large amounts of data, such as those arising from various modalities and process including, video imaging, sound, data mining, financial data, digital applications, and many other applications producing an ever increasing amount of data. Indeed, a growing computational power of many computing platforms has given rise to some very unique compression algorithms, some of which have become quite prevalent and popular among many users. Various compression techniques and algorithm, such as Huffman algorithms, LZW algorithm and/or Run Length Encoding may each be adapted to address different applications and/or different data structures, such as video, sound, and so forth.
Although such common compression techniques are effective when storing certain data, some of them involve substantial processing, particularly, during a decompression of the data, that is, when the compressed data is retrieved. For example, in Huffman decoding, a processor normally reads the data twice in order to build and/or retrieve a decoding key generally considered an inherent and integral part of the stored data. Hence, such decoding generally can take substantial time and could involve considerable processing overhead and time. This could further burden and/or strain the processing system, as well as lead to increase in costs and resources. Furthermore, certain encoding techniques, such as Huffman encoding, may involve and/or depend on various sensitive and lengthy statistical processes, also requiring substantial processing time, again, leading to similar shortcomings mentioned above.
Still, more significantly, conventional digital data compression/decompression techniques, such as those mentioned above, may not be very well suited for handling data generated by or that is accessible through dynamical networks, particularly, those through which data continually flows and/or is gathered in real time and on-the-fly between users endpoint, servers, processors and the like. Thus, the aforementioned data compression techniques may inherently be too rigid or otherwise not provide enough flexibility for generally accommodating efficient and reliable compression of transient data.